Socioemotional processing in female offenders - Resubmission 01

 描述（由申请人提供）：公众意识的提高伴随着最近的科学进步，了解精神疾病和某些形式的持续反社会行为之间的关系。这引发了对可能的干预措施进行研究的呼声， 预防措施在这一竞技场中，研究的一个关键障碍是过时的、描述性的精神病学结构分类法，这种分类法具有重叠的精神病学，并且很少整合来自神经科学研究的新兴知识。在内化和外化精神病理学的框架内表征的一系列特征和症状是法医环境中常见的几种精神病学结构的特征。这将是至关重要的持续进展，以确定病理学的基本特征，密切配合特定的神经生物学系统的认知加工领域的基础。其中，管理社会处理的系统，包括情绪相关的认知和观点采择，与精神病理学导致的反社会结果特别相关。我们的研究团队以前曾探索过社会情感处理的领域，因为它们与大型法医男性样本中的精神病特征有关。在这里，我们建议在女性法医样本中扩展这项工作。此外，我们整合了更广泛的维度结构的病理学在社会情感的处理，通过检查功能的精神病特征，以及边缘型人格障碍。 我们的研究策略是利用功能性磁共振成像来研究动态面部情感处理、从社会情境中推断情感状态和情感视角的神经回路。这些数据将为我们提供有关这些过程中性别差异的基本信息，以及病理学的关键特征是否与这些回路中的变异唯一相关。此外，我们将研究这些回路中的变异的效用，以预测不良的行为结果，包括反社会行为，药物滥用和自杀。重要的是，为了确定预测不良结果的关键特征，我们计划将传统的分层建模程序与更先进的数据驱动方法进行比较。传统的方法利用通过先前的神经成像工作识别的感兴趣区域，结合感兴趣的心理特征和其他关键的人口统计变量。先进的数据驱动方法利用独立成分分析来确定大脑活动的关键功能网络，并利用机器学习方法来选择构建适当模型所必需的特征。比较这些方法将为我们计划的未来努力提供信息，以制定补救策略并在神经学水平和行为水平上评估疗效。这些都是重要的，渐进的步骤，朝着更大的转化目标，以制定改进的，有针对性的治疗策略，由新兴的神经科学。

 DESCRIPTION (provided by applicant): Increasing public awareness has accompanied recent scientific progress understanding the relationship between mental illness and some forms of persistent antisocial behavior. This has incited calls for research into possible interventions and preventive measures. A critical barrier to research in this arena has been an outdated, descriptive taxonomy of psychiatric constructs with overlapping symptomatology and little integration of emerging knowledge from neuroscience research. An array of traits and symptoms characterized within the framework of internalizing and externalizing psychopathology are features of several psychiatric constructs common in forensic settings. It will be essential for continued progress to identify basic features of pathology that are closely aligned with specific neurobiological systems underlying domains of cognitive processing. Among these, systems governing social processing, including emotion-related cognition and perspective-taking are particularly relevant in antisocial outcomes due to psychopathology. Our research team has previously explored the domains of social-affective processing as they relate to psychopathic traits in a large, forensic male sample. Here we propose to extend this work in a female forensic sample. Further, we integrate a wider array of dimensional constructs of pathology in socio-affective processing by examining features of psychopathic traits as well borderline personality disorder. Our research strategy utilizes functional magnetic resonance imaging for the investigation of neural circuits involved in dynamic facial affective processing, inferring affective states from social situations, and emotional perspective-taking. These data will provide us with essential information about gender differences in these processes, and whether critical features of pathology are uniquely related to variation in these circuits. Furthermore, we will examine the utility of variation within these circuits to predict poor behavioral outcomes of interest including antisocial behavior, substance abuse, and suicide. Importantly, to determine key features predictive of poor outcomes, we plan to compare traditional hierarchical modeling procedures with more advanced data-driven approaches. Traditional approaches utilize regions of interest identified through prior neuroimaging work, combined with psychological traits of interest and other key demographic variables. Advanced data-driven approaches utilize Independent Component Analysis for determining key functional networks of brain activity, and utilize machine learning approaches for selecting features essential for building appropriate models. Comparing these approaches will inform our planned future efforts for developing remediation strategies and evaluating efficacy at both a neurological level as well as behavioral level. These are essential, incremental steps toward a larger translational goal to develop improved, targeted treatment strategies informed by emerging neuroscience.